What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that aim to reliably and efficiently deploy and maintain machine learning models in production. It bridges the gap between Data Science and Operations, enabling faster iteration and improved model performance. It's about treating ML model development like any other software development process – with a focus on automation, collaboration, and continuous improvement.
Key Pillars of MLOps
- Version Control: Track changes to your code, models, and data.
- Continuous Integration/Continuous Deployment (CI/CD): Automate the testing and deployment pipeline.
- Model Monitoring: Track model performance and identify issues like data drift.
- Infrastructure as Code: Manage your ML infrastructure using code.
- Collaboration: Foster a collaborative environment between Data Scientists, Engineers, and Operations teams.
Steps in an MLOps Workflow
- Data Collection & Preparation: Gather, clean, and transform data.
- Model Training: Train your model using appropriate algorithms.
- Model Evaluation: Assess model performance on held-out data.
- Model Deployment: Deploy the model to a production environment.
- Model Monitoring & Maintenance: Continuously monitor and update the model.